Delete audio.py

audio.py

@@ -1,221 +0,0 @@
-# coding=utf-8
-# Copyright 2021 The HuggingFace Datasets Authors and the current dataset script contributor.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-""" Common Voice Dataset"""
-
-
-import os
-
-import datasets
-from datasets.tasks import AutomaticSpeechRecognition
-
-
-_DATA_URL = "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-6.1-2020-12-11/{}.tar.gz"
-
-_CITATION = """\
-@inproceedings{,
-  author = },
-  title = {},
-  booktitle = {},
-  pages = {},
-  year = 
-}
-"""
-
-_DESCRIPTION = """\
-"""
-
-_HOMEPAGE = "https://Audio.mozilla.org/en/datasets"
-
-
-
-class AudioConfig(datasets.BuilderConfig):
-    """BuilderConfig for Audio."""
-
-    def __init__(self, name, sub_version, **kwargs):
-        """
-        Args:
-          data_dir: `string`, the path to the folder containing the files in the
-            downloaded .tar
-          citation: `string`, citation for the data set
-          url: `string`, url for information about the data set
-          **kwargs: keyword arguments forwarded to super.
-        """
-        self.sub_version = sub_version
-        self.language = kwargs.pop("language", None)
-        self.date_of_snapshot = kwargs.pop("date", None)
-        self.size = kwargs.pop("size", None)
-        self.validated_hr_total = kwargs.pop("val_hrs", None)
-        self.total_hr_total = kwargs.pop("total_hrs", None)
-        self.num_of_voice = kwargs.pop("num_of_voice", None)
-        description = f"Common Voice speech to text dataset in {self.language} version {self.sub_version} of {self.date_of_snapshot}. The dataset comprises {self.validated_hr_total} of validated transcribed speech data from {self.num_of_voice} speakers. The dataset has a size of {self.size}"
-        super(AudioConfig, self).__init__(
-            name=name, version=datasets.Version("6.1.0", ""), description=description, **kwargs
-        )
-
-
-class Audio(datasets.GeneratorBasedBuilder):
-
-    DEFAULT_WRITER_BATCH_SIZE = 1000
-    BUILDER_CONFIGS = [
-        AudioConfig(
-            name=lang_id,
-            language=_LANGUAGES[lang_id]["Language"],
-            sub_version=_LANGUAGES[lang_id]["Version"],
-            date=_LANGUAGES[lang_id]["Date"],
-            size=_LANGUAGES[lang_id]["Size"],
-            val_hrs=_LANGUAGES[lang_id]["Validated_Hr_Total"],
-            total_hrs=_LANGUAGES[lang_id]["Overall_Hr_Total"],
-            num_of_voice=_LANGUAGES[lang_id]["Number_Of_Voice"],
-        )
-        for lang_id in _LANGUAGES.keys()
-    ]
-
-    def _info(self):
-        features = datasets.Features(
-            {
-                "id": datasets.Value("string"),
-                "path": datasets.Value("string"),
-                "audio": datasets.Audio(sampling_rate=48_000),
-                "sentence": datasets.Value("string"),
-                "up_votes": datasets.Value("int64"),
-                "down_votes": datasets.Value("int64"),
-                "age": datasets.Value("string"),
-                "gender": datasets.Value("string"),
-                "accent": datasets.Value("string"),
-                "locale": datasets.Value("string"),
-                "segment": datasets.Value("string"),
-            }
-        )
-
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,
-            supervised_keys=None,
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION,
-            task_templates=[AutomaticSpeechRecognition(audio_column="audio", transcription_column="sentence")],
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        # Download the TAR archive that contains the audio files:
-        archive_path = dl_manager.download(_DATA_URL.format(self.config.name))
-
-        # First we locate the data using the path within the archive:
-        path_to_data = "/".join(["cv-corpus-6.1-2020-12-11", self.config.name])
-        path_to_clips = "/".join([path_to_data, "clips"])
-        metadata_filepaths = {
-            split: "/".join([path_to_data, f"{split}.tsv"])
-            for split in ["train", "test", "dev", "other", "validated", "invalidated"]
-        }
-        # (Optional) In non-streaming mode, we can extract the archive locally to have actual local audio files:
-        local_extracted_archive = dl_manager.extract(archive_path) if not dl_manager.is_streaming else None
-
-        # To access the audio data from the TAR archives using the download manager,
-        # we have to use the dl_manager.iter_archive method.
-        #
-        # This is because dl_manager.download_and_extract
-        # doesn't work to stream TAR archives in streaming mode.
-        # (we have to stream the files of a TAR archive one by one)
-        #
-        # The iter_archive method returns an iterable of (path_within_archive, file_obj) for every
-        # file in the TAR archive.
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "local_extracted_archive": local_extracted_archive,
-                    "archive_iterator": dl_manager.iter_archive(
-                        archive_path
-                    ),  # use iter_archive here to access the files in the TAR archives
-                    "metadata_filepath": metadata_filepaths["train"],
-                    "path_to_clips": path_to_clips,
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "local_extracted_archive": local_extracted_archive,
-                    "archive_iterator": dl_manager.iter_archive(
-                        archive_path
-                    ),  # use iter_archive here to access the files in the TAR archives
-                    "metadata_filepath": metadata_filepaths["test"],
-                    "path_to_clips": path_to_clips,
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={
-                    "local_extracted_archive": local_extracted_archive,
-                    "archive_iterator": dl_manager.iter_archive(
-                        archive_path
-                    ),  # use iter_archive here to access the files in the TAR archives
-                    "metadata_filepath": metadata_filepaths["dev"],
-                    "path_to_clips": path_to_clips,
-                },
-            ),
-        ]
-
-    def _generate_examples(self, local_extracted_archive, archive_iterator, metadata_filepath, path_to_clips):
-        """Yields examples."""
-        data_fields = list(self._info().features.keys())
-
-        # audio is not a header of the csv files
-        data_fields.remove("audio")
-        path_idx = data_fields.index("path")
-
-        all_field_values = {}
-        metadata_found = False
-        # Here we iterate over all the files within the TAR archive:
-        for path, f in archive_iterator:
-            # Parse the metadata CSV file
-            if path == metadata_filepath:
-                metadata_found = True
-                lines = f.readlines()
-                headline = lines[0].decode("utf-8")
-
-                column_names = headline.strip().split("\t")
-                assert (
-                    column_names == data_fields
-                ), f"The file should have {data_fields} as column names, but has {column_names}"
-                for line in lines[1:]:
-                    field_values = line.decode("utf-8").strip().split("\t")
-                    # set full path for mp3 audio file
-                    audio_path = "/".join([path_to_clips, field_values[path_idx]])
-                    all_field_values[audio_path] = field_values
-            # Else, read the audio file and yield an example
-            elif path.startswith(path_to_clips):
-                assert metadata_found, "Found audio clips before the metadata TSV file."
-                if not all_field_values:
-                    break
-                if path in all_field_values:
-                    # retrieve the metadata corresponding to this audio file
-                    field_values = all_field_values[path]
-
-                    # if data is incomplete, fill with empty values
-                    if len(field_values) < len(data_fields):
-                        field_values += (len(data_fields) - len(field_values)) * ["''"]
-
-                    result = {key: value for key, value in zip(data_fields, field_values)}
-
-                    # set audio feature
-                    path = os.path.join(local_extracted_archive, path) if local_extracted_archive else path
-                    result["audio"] = {"path": path, "bytes": f.read()}
-                    # set path to None if the audio file doesn't exist locally (i.e. in streaming mode)
-                    result["path"] = path if local_extracted_archive else None
-
-                    yield path, result